Qwen/Qwen2.5-VL-32B-Instruct
alibaba/qwen2-5-vl-32b-instruct제공: Alibaba (Qwen) · 패밀리: qwen · 출시 2025-03-24 · 지식 컷오프: 2024-09
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
기능
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding68
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability6/10
Agents81
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability6/10
JSON / structured output99
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency81
- Headline price (log-scaled)76/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Vision83
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability6/10
Production-readiness86
- Number of independent providers30/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.36 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.72 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.21 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.62 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.73 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
가격 상세
추천 가격 제공자: io-net · Qwen/Qwen2.5-VL-32B-Instruct
6곳 제공사에서 이용 가능
| 제공자 | 제공자 모델 ID | 입력 / 1M | 출력 / 1M | 컨텍스트 | 출시일 |
|---|---|---|---|---|---|
| SiliconFlow (China) siliconflow-cn | Qwen/Qwen2.5-VL-32B-Instruct | $0.270 | $0.270 | 131K | 2025-03-24 |
| IO.NET io-net | Qwen/Qwen2.5-VL-32B-Instruct | $0.050 | $0.220 | 32K | 2024-11-01 |
| Chutes chutes | Qwen/Qwen2.5-VL-32B-Instruct | $0.054 | $0.217 | 16K | 2025-12-29 |
| Meganova meganova | Qwen/Qwen2.5-VL-32B-Instruct | $0.200 | $0.600 | 16K | 2025-03-24 |
| SiliconFlow siliconflow | Qwen/Qwen2.5-VL-32B-Instruct | $0.270 | $0.270 | 131K | 2025-03-24 |
| LLM Gateway llmgateway | qwen2-5-vl-32b-instruct | $0.300 | $0.300 | 131K | 2025-03-15 |
제공자 간 데이터 불일치
- context_window varies: 131000, 131072, 16384, 32000
- release_date varies (span 423d): 2024-11-01, 2025-03-15, 2025-03-24, 2025-12-29
제공자별로 이 모델의 값이 다릅니다. 위의 핵심 정보는 대표 제공자 기준이며, 제공자별 상세는 표를 참고하세요.
Frequently asked questions
How much does Qwen/Qwen2.5-VL-32B-Instruct cost?
Qwen/Qwen2.5-VL-32B-Instruct costs $0.050 per 1M input tokens and $0.220 per 1M output tokens, sourced from io-net. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen/Qwen2.5-VL-32B-Instruct?
Qwen/Qwen2.5-VL-32B-Instruct has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen/Qwen2.5-VL-32B-Instruct support tool calling?
Yes. Qwen/Qwen2.5-VL-32B-Instruct supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Qwen/Qwen2.5-VL-32B-Instruct support structured output / JSON mode?
Yes. Qwen/Qwen2.5-VL-32B-Instruct supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Qwen/Qwen2.5-VL-32B-Instruct accept image input?
Yes. Qwen/Qwen2.5-VL-32B-Instruct accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Alibaba (Qwen)'s docs for the exact rule.
Is Qwen/Qwen2.5-VL-32B-Instruct open-weight?
No. Qwen/Qwen2.5-VL-32B-Instruct is a proprietary model — only Alibaba (Qwen) (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Qwen/Qwen2.5-VL-32B-Instruct?
If Qwen/Qwen2.5-VL-32B-Instruct doesn't fit, consider Qwen3.5 397B-A17B, Qwen3 32B, Qwen3 235B A22B Instruct 2507. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Alibaba (Qwen) models
- Qwen3.5 397B-A17B$0.60 in / $3.60 out
- Qwen3 32B$0.70 in / $2.80 out
- Qwen3 235B A22B Instruct 2507$0.10 in / $0.10 out
- Qwen3-Coder 480B-A35B Instruct$1.50 in / $7.50 out
- Qwen3-235B-A22B-Thinking-2507$0.10 in / $0.10 out
Capability lists this model is in
마지막 업데이트:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.